This PDE practice question tests your understanding of operationalizing machine learning models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company wants to monitor the performance of a deployed model in production. Which metric indicates that the model's predictions are degrading?
Increase in prediction error rate
Error rate reflects model accuracy.
Increase in prediction latency
Why wrong: Latency is performance, not accuracy.
Decrease in throughput
Why wrong: Throughput is capacity.
Increase in number of requests
Why wrong: Request count is traffic volume.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Increase in prediction error rate
An increase in prediction error rate directly indicates that the model's outputs are deviating from the expected or ground-truth values, signaling degradation in predictive performance. This metric captures the core concept of model drift, where the statistical properties of the input data or the relationship between features and labels change over time, leading to less accurate predictions. In production ML monitoring, tracking error rate (e.g., classification accuracy, RMSE) is the primary method to detect when a model needs retraining or updating.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Increase in prediction error rate
Why this is correct
Error rate reflects model accuracy.
Related concept
Read the scenario before looking for a memorised answer.
Increase in prediction latency
Why it's wrong here
Latency is performance, not accuracy.
Decrease in throughput
Why it's wrong here
Throughput is capacity.
Increase in number of requests
Why it's wrong here
Request count is traffic volume.
Common exam traps
Google Cloud often tests the distinction between operational metrics (latency, throughput) and model performance metrics (error rate), trapping candidates who confuse system health with prediction quality.
Detailed technical explanation
Under the hood, model degradation is often detected by monitoring the prediction error rate against a held-out validation set or through online evaluation using ground-truth labels that arrive with a delay. In production systems, this is implemented via data drift detectors (e.g., using KL divergence or PSI on feature distributions) and concept drift detectors (e.g., Page-Hinkley test or ADWIN) that trigger alerts when the error rate exceeds a threshold. A real-world scenario involves a credit scoring model where an increase in false positive rate (a type of prediction error) over time indicates that the model is no longer accurately assessing risk due to changing economic conditions.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
The correct answer is: Increase in prediction error rate — An increase in prediction error rate directly indicates that the model's outputs are deviating from the expected or ground-truth values, signaling degradation in predictive performance. This metric captures the core concept of model drift, where the statistical properties of the input data or the relationship between features and labels change over time, leading to less accurate predictions. In production ML monitoring, tracking error rate (e.g., classification accuracy, RMSE) is the primary method to detect when a model needs retraining or updating.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.
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